Computer based education methods and apparatus

ABSTRACT

A method for dynamically allocating server resources includes receiving a request from a client system, wherein the request comprises a request for a first set of streaming data, providing from the server to the client system a first portion of streaming data from the first set of streaming data, wherein the first portion is associated with a first quality of service level, receiving user activity data from the client system for the first portion of the streaming data, determining a second quality of service level for a second portion of the streaming data from the first set of streaming data, providing from the server to the client system the second portion of streaming data from the first set of streaming data, wherein the second portion provided with the second quality of service level, and wherein the first quality of service level is different from the second quality of service level.

CROSS REFERENCE TO RELATED PATENT APPLICATION

This application is a continuation of U.S. Application No. 16/268,282,filed Feb. 5, 2019, now issued as U.S. Pat. No. 11,533,272 on Dec. 20,2022, which claims priority to U.S. Provisional Patent Application No.62/627,143, filed on Feb. 6, 2018, which is hereby incorporated byreference in its entirety.

BACKGROUND OF INVENTION

The present invention relates to efficient network and computer hardwaremanagement. More specifically, the present invention relates toefficient resource management in a distributed computing environment.The present invention also relates generally to processing techniquesfor a distributed learning environment having course materials. In someembodiments, the invention provides a method and system for using ablock chain configured on a public ledger for securing user informationassociated with an on-line course, hybrid on-line and on-campus course,a certification or degree, or other information in the field ofeducation. More particularly, the invention provides a method and systemusing data capturing devices configured with artificial intelligencetechniques for managing computing environment resources. The inventionmay provide securing storing a certification associated with a learningprocess associated with such method and system. In some embodiments, theinvention provides a method and apparatus for generating and/orproviding a crypto-coin using a learning module. Merely by way ofexample, the invention has been applied to a mobile computing deviceconfigured to a worldwide network of computers, however, the inventionhas many other applications.

Education is the process of facilitating learning, or the acquisition ofknowledge, skills, values, beliefs, and habits. Educational methodsinclude storytelling, discussion, teaching, training, and directedresearch. Education frequently takes place under the guidance ofeducators, but learners may also educate themselves. Education can takeplace in formal or informal settings and any experience that has aformative effect on the way one thinks, feels, or acts may be considerededucational. The methodology of teaching is called pedagogy.https://en.wikipedia.org/wiki/Education.

Education originally occurred through a one by one basis between teacherand student or master and apprentice or partner and associate.Classrooms eventually took over to teach children in masses frompre-school to higher education. Most recently, education has beenimplemented on-line via the Internet to facilitate learning forstudents. The inventor of the present invention believes there are somedrawbacks with on-line learning. Some drawbacks for students includethat because on-line students tend to learn in a solitary way, there islittle, if any way to provide individualized learning and to helpstudents succeed. Some drawbacks for the providers of on-line educationsystems includes that providers must purchase enough hardware orsoftware services to serve the worst-case scenarios, e.g. every student.

Although education has progressed, it is desired that techniques toovercome difficulties in education, and more particularly learning aredesired.

SUMMARY

According to the present invention, techniques related to coursematerials. In particular, the invention provides a method for using ablock chain configured on a public ledger for securing user informationassociated with an on-line course, a certification or degree, or otherinformation in the field of education. More particularly, the inventionprovides a method and system using data capturing devices configuredwith artificial intelligence techniques, and then securing and storing acertification associated with a learning process associated with suchmethod and system. More particularly, the invention provides a methodand apparatus for generating a crypto-coin using a learning module.Merely by way of example, the invention has been applied to a mobilecomputing device configured to a worldwide network of computers,however, the invention has many other applications.

In an example, the invention provides a computing apparatus. Theapparatus has a bus device configured to be an interface fortransmission of data. The apparatus has a micro processing devicecoupled to the bus device, an image capturing device coupled to the busdevice, a memory resource coupled to the bus device, a wallet deviceprovided in the memory resource or insertable as a separate device to becoupled to the bus device, a network interface coupled to the busdevice, a power supply coupled to each of the bus device, the microprocessing device, the image capturing device, the memory resource, andthe network interface. The apparatus has a learning module coupled tothe bus device. In an example, the learning module comprises a coursemodule configured to output a course directed to a subject, the coursecomprising a plurality of templates and a video associated with thesubject. The module has a sensor input handler coupled to a plurality ofsensing devices, a video input handler coupled to the bus device, anartificial intelligence (“AI”) module comprising a plurality of nodesnumbered from 1 through N, where N is an integer of 10,000,000 or less,and an AI output each of the nodes being coupled the sensor inputhandler, the video input handler, or another input handler, an outputhandler coupled to the processing device, and configured to the AIoutput, a feedback process coupled to the output handler to intakeinformation from the AI output and configured to reduce a number ofcycles executed by the micro processing device from a firstpredetermined number range to a second predetermined number range toreduce a power consumption of the micro processing device from a firstpredetermined power range to a second predetermined power range. Theapparatus has a miner module coupled to an output of the learning moduleto initial a process to determine if a coin device associated with auser of the course module by a user, issuing the coin device to theuser, and storing the coin device in the wallet device, while the coindevice is configured in an encrypted form.

In an example, the present invention provides a meta data processingapparatus for processing sensor inputs and providing feedback to a userfor an on-line course. The apparatus has a housing configured with adisplay device. In an example, the display device is coupled to an inputdevice for communicating text information from a user. The device has aprocessing device, such as a central processing unit, graphicsprocessing unit, digital signal processing unit, micro controller orothers.

In an example, the apparatus has a network interface coupled to theprocessing device. In an example, the network interface is configured tocouple to a worldwide network of computers or a local network ofcomputers. The apparatus has a memory resource coupled to the processingdevice and an application comprising a course module. In an example, thecourse module comprises a plurality of templates and at least one videofile, and processes, each of which may be desirably tailored to a userbased upon feedback from various processing modules.

In an example, the apparatus has an image capturing device coupled tothe housing and configured to the processing device. In an example, theimage capturing device is configured to capture an image of at least afacial expression in a first format of the user while interacting withthe course module. The image capturing device can be a high-resolutioncamera that is suitable for capturing the image and has sufficientpixels to be processed.

In an example, the apparatus has a plurality of sensors for identifyinga spatial orientation of the user while interacting with the coursemodule. In an example, the sensor devices or plurality of external datacapturing devices comprises a camera, a keyboard, an accelerometersensor, an rf sensor, a gyroscope, a chemical sensor, a temperaturesensor, a microphone, or other input device. Of course, there can beother variations, modifications, and alternatives.

In various embodiments, the apparatus may include a mixed reality orvirtual reality headset that captures the user data using sensors fromwithin a headset (e.g. Microsoft HoloLens and Mixed Reality platform,Magic Leap platform, Google Daydream, etc.) or that captures user datafrom a headset using external sensors, (e.g. HTC Vive, Oculus Rift)Various embodiments of headsets may provide spatial orientation dataincluding where the user is viewing within an image (e.g. a lecturer,white board, etc.), where they are gazing within an image (e.g.equation, graph or diagram, etc.), duration of time viewing materials(e.g. reading a slide, or .pdf, etc.); voice data (e.g. a user repeatinga foreign language phrase); and the like.

In some examples, the apparatus uses a variety of sensors, e.g.accelerometers, a camera, a pointing device, etc., to monitor the user'sactions, while the user interacts with the course module. Depending uponthe type of user action, an inference can be made as to whether the useris actively paying attention and/or whether the user is activelylearning. Based upon such inferences, a number of actions may be takento provide positive feedback to the user. The positive feedback mayinclude the user being given “activity credits” or points that areconvertible into prizes possibly having monetary value (e.g. .mp3download, coffee cards, etc.); the user being given a crypto currency;the user being assigned to a blockchain mining process/rig; and thelike, as will be described further below. In some examples, the user'sactions may lead to an inference that the user is not actively payingattention and/or that the user is not actively learning. Based upon suchinferences, a number of actions may be taken to regain the user'sattention, such as modifying a volume of audio, modifying colors of apresentation, retrieving and presenting related or unrelated content tothe user, and the like.

In some examples, the user's actions may lead to an inference that theuser is not actively paying attention and/or that the user is notactively learning. Based upon such inferences, a number of actions maybe taken to reduce computer or network resources, such as reducing framerate or resolution of video, reducing a bit-rate of audio or video,decreasing quality of service, increasing latency, reducingcommunication data rate, and the like. In some embodiments, bymonitoring the user's actions per each course module, e.g. during alecture or series of lectures, a host system may determine patterns frommultiple users' actions. These patterns may be used to determine anumber of computer or network resources required for each lecture orseries of lectures, as will be described below.

In an example, the natural language processor is configured to processthe text information to identify a characteristic of the user inassociation with the course. Additionally, the apparatus has anartificial intelligence module coupled to the processing device. In anexample, the artificial intelligence module comprises a neural networkmodule comprising a plurality of nodes. In an example, the plurality ofnodes can be numbered from 1-N, where N is an integer greater than 10and less than 10,000,000, and possibly greater, depending upon theexample. The plurality of nodes are coupled to respective sensors, imagecapturing device, natural language processor, or other informationreceiving devices, and an output. Of course, there can be othervariations, modifications, and alternatives.

In an example, the apparatus has an output handler coupled to the outputof the neural network module, the output handler configured to providefeedback to the user. The feedback comprises a plurality ofcharacteristics to allow the user to achieve a predetermined scorewithin a range for the course.

The above examples and implementations are not necessarily inclusive orexclusive of each other and may be combined in any manner that isnon-conflicting and otherwise possible, whether they be presented inassociation with a same, or a different, embodiment or example orimplementation. The description of one embodiment or implementation isnot intended to be limiting with respect to other embodiments and/orimplementations. Also, any one or more function, step, operation, ortechnique described elsewhere in this specification may, in alternativeimplementations, be combined with any one or more function, step,operation, or technique described in the summary. Thus, the aboveexamples implementations are illustrative, rather than limiting.

DESCRIPTION OF THE DRAWINGS

FIG. 1A is a simplified diagram of a meta processing system for learningaccording to an example of the present invention.

FIG. 1 is a simplified diagram with a network system according to anexample of the present invention.

FIG. 2 is a more detailed diagram of a processing engine with inputdevices according to an example of the present invention.

FIG. 3 is a more detailed diagram of a processing system according to anexample of the present invention.

FIG. 4 is a more detailed diagram of an alternative processing systemaccording to an example of the present invention.

FIG. 5 is a more detailed diagram of an alternative processing systemaccording to an example of the present invention.

FIG. 6 is a simplified flow diagram of an integrated data processingprocess according to an example of the present invention.

FIG. 7 is a simplified flow diagram of a power management and miningprocess according to an example of the present invention.

DETAILED DESCRIPTION OF THE EXAMPLES

According to the present invention, techniques related to coursematerials are provided. In particular, the invention provides a methodand system for using a block chain configured on a public ledger forsecuring user information associated with an on-line course, acertification or degree, or other information in the field of education.More particularly, the invention provides a method and system using datacapturing devices configured with artificial intelligence techniques,and then securing storing a certification associated with a learningprocess associated with such method and system. More particularly, theinvention provides a method and apparatus for generating a crypto-coinusing a learning module. Merely by way of example, the invention hasbeen applied to a mobile computing device configured to a world-widenetwork of computers, however, the invention has many otherapplications.

In an example, one or more of the definitions may be used in thefollowing description of the specification.

EIs or Education Institutions means a non-profit, for-profitorganizations, or entitie(s) that offer education or training.

IACs or Institutional Accreditation Credentials means verified receiptof passed reviews or diligence by qualified bodies, as defined by therelevant society, committee, regulatory agency, nonprofit body orentity, to the field of learning, standards, or other regulatory body.

EPs or Educational Products means courses or other educationalofferings.

LIs means Learner Identification.

EICs or EI coins or tradeable currencies or points for learning productsmeans a point or monetary entity associated with learning products.

In an example, the present invention provides a trustworthyauthentication technique. In an example, the technique is foruniversities and colleges (that contain the key expertise andscholarship) to qualify, codify and ratify information that is“canonical” and therefore meritorious of inclusion in higher educationor an educational program. In an example, all institutions, courses, orinstructors will go through accreditation, which of course is run by themembers of the system, not an outside entity.

In an example, the technique provides for creating a block chain ofuniversities or colleges or entities that have been authenticated andaccredited. In an example, other aspects that can be configured into ablock chain include, but are not limited to, course materials,attendance records, transcripts, matriculation data, student data,faculty data, assessment scores, credit hours awarded, grades,acquisition of coins and other information. Rather than being in acentral repository, any and all such information will be distributed ona public ledger across a worldwide network of computers in an encryptedmanner. A key will allow an authorized entity to access such informationusing conditions if needed, such as time, place, and duration. Ofcourse, there can be other variations, modifications, and alternatives.

In various embodiments a system is disclosed having a network coupled toa remote server and a number of clients. In various embodiments, networkmay be the Internet, and may use wired and wireless communicationsmechanisms to transmit data between remote server and clients. Remoteserver may include a number of virtual and/or physical computer servers,and may be implemented on a cloud-based service, such as Amazon WebServices, or the like.

Remote server may include a data server, an application server, aresource optimization server (e.g. quality of service), a securityserver, as well as functional modules, described below. In variousembodiments, data server may be used to provide data for (e.g. coursework, lectures, presentation, etc.) and store data from (e.g. userresponse data, user resource utilization data, etc.) clients.Application server may be used to communicate with applications (e.g.web browser, desktop application, smart device application, etc.)running upon clients, to provide data to clients, and to receive datafrom clients. A resource optimization server is coupled to applicationserver for receiving user resource utilization data (discussed furtherbelow) typically from each of clients and attempts to optimize theresources of remote server in response to the utilization data.Additionally, resource optimization server attempts to optimize the databandwidth allocated for each of clients, depending upon the respectiveutilization data. In various embodiments, security server may be used tosupport the user logging into server. Other modules are described below.

In various embodiments, the data server, application server, theresource optimization server, security server, and other modules may bevirtual machines upon a physical server, may be virtual machinesimplemented upon different physical servers, or combinations thereof. Insome embodiments, clients may include laptop computers (e.g. AppleMacbook, Microsoft Surface), smart devices (e.g. Apple iPhone, AppleiPad), virtual or mixed reality systems (e.g. Oculus Rift, GoogleDaydream, Microsoft HoloLens, Magic Leap device), wearable device (e.g.Apple Watch, LG G Watch), or the like.

FIG. 1A is a simplified diagram of a meta processing system for learningaccording to an example of the present invention. As shown, the systemhas a course module, which includes templates, video, and otherinformation. The course can be directed to a variety of subjectsincluding but not limited to science, math, English, history,psychology, engineering, business, finance, accounting, or any othercollege course subject.

As shown, the course module is configured with a plurality of sensordevices, an image capturing device, data input device, virtual reality(VR) (including augmented reality, mixed reality), or the like. In anexample, the sensor devices or plurality of external data capturingdevices comprises a camera, a keyboard, an accelerometer sensor, an rfsensor, a gyroscope, a chemical sensor, a temperature sensor, amicrophone, VR device, or other input device. Of course, there can beother variations, modifications, and alternatives.

In an example, the system has a meta processor. The meta processorincludes an artificial intelligence process, a natural language process,and neural net process each of which can be run configured with eachother or independently from each other.

In an example, the invention provides a method of using the meta moduleprocess of capturing data and processing the data for feedback to auser, e.g., learner, student, or other human entity. In an example, themethod includes providing information related to a course in a firstformat. In an example, the course can be sourced from an off-lineclass-room course.

In an example, the method includes transferring the information in thefirst format to a server device. The server device can be coupled to aplurality of computers on a worldwide network of computers, such as theInternet. In an example, the method includes storing the information inthe first format into a memory resource coupled to the server device.

In an example, the method includes processing the information related tothe course on the server device to convert the course into a secondformat, the second format comprising a plurality of templates and avideo. The second format is suitable for using the course in an on-linemanner.

The method configures the course in the second format with a metamodule. As noted, the meta module comprises an artificial intelligencemodule. In an example, the artificial intelligence module comprises aplurality of input interfaces each of the input interfaces coupled to anode. In an example, each of the nodes comprises a weighing function. Inan example, each of the nodes is coupled to an output interface.

In an example, the artificial intelligence module comprises a neuralnetwork process configured on a neural network process, wherein theplurality of nodes comprises at least 1 million nodes. In an example,the artificial intelligence module comprises a neural network processconfigured on a processing device.

In an example, the method includes configuring the course coupled to theartificial intelligence module to a plurality of external data capturingdevices. Each of the plurality of external data captures devices beingspatially disposed on a mobile computing device. In an example, themobile computing device is coupled to the worldwide network ofcomputers.

In an example, the method includes initiating use of the course in thesecond format coupled to the artificial intelligence module from themobile computing device and capturing data from a user of the mobilecomputing device from each of the plurality of external data capturingdevices, while the user is actively interacting with the course in thesecond format from the mobile computing device.

In an example, the method includes transferring data from each of theplurality of external data capturing devices from the mobile computingdevice to the artificial intelligence module and, thereafter, outputtinga feedback from the artificial intelligence module to the user of thecourse.

In an example, the method also includes finalizing use of the course inthe second format; and initiating a test associated with the course.Optionally, the method includes finalizing use of the course in thesecond format; initiating a test associated with the course; passing thetest; and transferring a credit for the course to the user of the mobiledevice and the course. Of course, there can be other variations,modifications, and alternatives.

Optionally, the method can also include transferring spatial movementinformation from a wearable device to the mobile computing device. Thewearable device can be a watch, a suit, a vest, a headset, a pair ofglasses, a pair of pants, shoes, or other wearable device. The wearabledevice can include a plurality of sensing devices spatially disposed onthe device. A wireless or wired transceiver or transmitter can transmitinformation from each of the sensors to the meta processor.

In an example, the method includes finalizing use of the course in thesecond format. The method includes initiating a test associated with thecourse and passing the test (or taking it over if the user does not passthe course). The method includes transferring a credit for the course tothe user of the mobile device and the course, configuring the creditwith a time and date stamp and other information into a block ofinformation, and adding the block of information into a chain of otherblocks of information to form a block chain associated with the user inan encrypted format distributed on a public ledger configurationprovided on a worldwide network of computers.

In an example, the course module and other elements can be implementedusing a mobile device. In an example, the course module and otherelements can be delivered using a mobile device. In an example, themobile device is a lap top computer, a tablet, an iPad, a Smart phone,or other mobile device. Of course, there can be other variations,modifications, and alternatives.

In an alternative example, the present invention provides a meta dataprocessing apparatus for processing sensor inputs and providing feedbackto a user for an on-line course. The apparatus has a housing configuredwith a display device. In an example, the display device is coupled toan input device for communicating text information from a user. Thedevice has a processing device, such as a central processing unit,graphics processing unit, digital signal processing unit, microcontroller or others.

In an example, the apparatus has a network interface coupled to theprocessing device. In an example, the network interface is configured tocouple to a world wide network of computers or a local network ofcomputers. The apparatus has a memory resource coupled to the processingdevice and an application comprising a course module. In an example, thecourse module comprises a plurality of templates and at least one videofile, and processes, each of which may be desirably tailored to a userbased upon feedback from various processing modules.

In an example, the apparatus has an image capturing device coupled tothe housing and configured to the processing device. In an example, theimage capturing device is configured to capture an image of at least afacial expression in a first format of the user while interacting withthe course module. The image capturing device can be a high-resolutioncamera that is suitable for capturing the image and has sufficientpixels to be processed.

In an example, the apparatus has a plurality of sensors for identifyinga spatial orientation of the user while interacting with the coursemodule. In an example, the sensor devices or plurality of external datacapturing devices comprises a camera, a keyboard, an accelerometersensor, an rf sensor, a gyroscope, a chemical sensor, a temperaturesensor, a microphone, or other input device. Of course, there can beother variations, modifications, and alternatives.

In an example, the apparatus has a natural language processor configuredfor processing information from the text information while the user isinteracting with the course module. In an example, the natural languageprocessor is configured to process the text information to identify acharacteristic of the user in association with the course. Additionally,the apparatus has an artificial intelligence module coupled to theprocessing device. In an example, the artificial intelligence modulecomprises a neural network module comprising a plurality of nodes. In anexample, the plurality of nodes can be numbered from 1-N, where N is aninteger greater than 10 and less than 10,000,000, and possibly greater,depending upon the example. The plurality of nodes are coupled torespective sensors, image capturing device, natural language processor,or other information receiving devices, and an output. Of course, therecan be other variations, modifications, and alternatives.

In an example, the apparatus has an output handler coupled to the outputof the neural network module, the output handler configured to providefeedback to the user. The feedback comprises a plurality ofcharacteristics to allow the user to achieve a predetermined scorewithin a range for the course.

In an example, the course is related to science, math, English, history,psychology, engineering, business, finance, accounting, or any othercollege course subject.

In an example, the apparatus includes a wearable device comprising a setof sensors to characterize movement, orientation, temperature, heartrate, breathing rate, oxygen, and other parameters of the user whileinteracting with the course, the wearable device being coupled to aninput of the artificial intelligence module.

In an example, the housing is shock proof. In an example, the housing iswater proof.

In an example, the neural network module is configured to receiveinformation from the image capturing device and output a learningcharacteristic of the user.

In an example, the neural network module is configured to receiveinformation associated with a facial expression and an eye response fromthe image capturing device and output a learning characteristic of theuser.

In an example, the method includes transferring a spatial locationinformation and a time information to the artificial intelligencemodule.

In a preferred example, an output handler coupled to the output of theneural network module, the output handler configured to provide feedbackto the user and configure the myamesiteTM course. The myamesite coursehas been configured with feedback to create a specialized orpersonalized course for the user or student. Further details of thepresent method, apparatus, and system can be found throughout thepresent specification and more particularly below.

FIG. 1 is a simplified diagram with a network system according to anexample of the present invention. As shown, the network system has acourse module, which is coupled to a client device 109, which is coupledto a world wide network of computers, such as the Internet 111. In anexample, the Internet comprises a plurality of server devices 113coupled to memory resource 115 or database. The system also includes aplurality of servers configured with a plurality of block chaininformation, as shown.

In an example, the block chain information can be provided by ablockchain formation, which can be described in Wikipedia.com. In anexample, a main chain consists of the longest series of blocks from thegenesis block to the current block. Orphan blocks exist outside of themain chain. In an example, a blockchain is a growing list of records,called blocks, which are linked using cryptography. Each block containsa cryptographic hash of the previous block, a timestamp, and transactiondata (generally represented as a merkle tree root hash or other type ofhash or configuration). By design, a blockchain is resistant tomodification of the data. The blockchain is an open, distributed ledgerthat can record transactions between two parties efficiently and in averifiable and permanent way. For use as a distributed ledger, ablockchain is typically managed by a peer-to-peer network collectivelyadhering to a protocol for inter-node communication and validating newblocks. Once recorded, the data in any given block cannot be alteredretroactively without alteration of all subsequent blocks, whichrequires consensus of the network majority. Although blockchain recordsare not unalterable, blockchains may be considered secure by design andexemplify a distributed computing system with high Byzantine faulttolerance. Decentralized consensus has therefore been claimed with ablockchain. Of course, there can be other variations, modifications, andalternatives.

FIG. 2 is a more detailed diagram of a processing engine with inputdevices according to an example of the present invention. In an example,the processing engine 201 is coupled to input devices 203, 205, 207,209, 211, 214, among others. The processing engine interfaces with amemory resource, such as storage 215.

In an example, the processing engine is configured with a meta dataprocessing apparatus for processing sensor inputs and providing feedbackto a user for an on-line course. The apparatus has a housing configuredwith a display device. In an example, the display device is coupled toan input device for communicating text information from a user. Thedevice has a processing device, such as a central processing unit,graphics processing unit, digital signal processing unit, microcontroller or others.

In an example, the apparatus has a network interface 207 coupled to theprocessing device. In an example, the network interface is configured tocouple to a world wide network of computers or a local network ofcomputers. The apparatus has a memory resource 215 coupled to theprocessing device and an application comprising a course module. In anexample, the course module comprises a plurality of templates and atleast one video file, and processes, each of which may be desirablytailored to a user based upon feedback from various processing modules.

In an example, the apparatus has an image capturing device coupled tothe housing and configured to the processing device. In an example, theimage capturing device is configured to capture an image of at least afacial expression in a first format of the user while interacting withthe course module. The image capturing device can be a high resolutioncamera that is suitable for capturing the image and has sufficientpixels to be processed.

In an example, the apparatus has a plurality of sensors for identifyinga spatial orientation of the user while interacting with the coursemodule. In an example, the sensor devices or plurality of external datacapturing devices comprises a camera, a keyboard, an accelerometersensor, an rf sensor, a gyroscope, a chemical sensor, a temperaturesensor, a microphone, or other input device. Of course, there can beother variations, modifications, and alternatives.

In an example, the apparatus has a natural language processor configuredfor processing information from the text information while the user isinteracting with the course module. In an example, the natural languageprocessor is configured to process the text information to identify acharacteristic of the user in association with the course. Additionally,the apparatus has an artificial intelligence module coupled to theprocessing device. In an example, the artificial intelligence modulecomprises a neural network module comprising a plurality of nodes. In anexample, the plurality of nodes can be numbered from 1-N, where N is aninteger greater than 10 and less than 10,000,000, and possibly greater,depending upon the example. The plurality of nodes are coupled torespective sensors, image capturing device, natural language processor,or other information receiving devices, and an output. Of course, therecan be other variations, modifications, and alternatives.

In an example, the apparatus has an output handler coupled to the outputof the neural network module, the output handler configured to providefeedback to the user. The feedback comprises a plurality ofcharacteristics to allow the user to achieve a predetermined scorewithin a range for the course.

In an example, the course is related to science, math, English, history,psychology, engineering, business, finance, accounting, or any othercollege course subject.

In an example, the apparatus includes a wearable device comprising a setof sensors to characterize movement, orientation, temperature, heartrate, breathing rate, oxygen, and other parameters of the user whileinteracting with the course, the wearable device being coupled to aninput of the artificial intelligence module.

In an example, the housing is shock proof.

In an example, the neural network module is configured to receiveinformation from the image capturing device and output a learningcharacteristic of the user.

In an example, the neural network module is configured to receiveinformation associated with a facial expression and an eye response fromthe image capturing device and output a learning characteristic of theuser.

In an example, the method includes transferring a spatial locationinformation and a time information to the artificial intelligencemodule.

In a preferred example, the an output handler coupled to the output ofthe neural network module, the output handler configured to providefeedback to the user and configure the myamesiteTM course. The myamesitecourse has been configured with feedback to create a specialized orpersonalized course for the user or student.

FIG. 3 is a more detailed diagram of a processing system 300 accordingto an example of the present invention. As shown, the system has a bus301 that communicates with input output devices 307, 309, 313. The inputoutput devices is coupled to camera 303, sensors 311, and internalsensors 305. The bus also has communication module 315, a digital signalprocessor 317, other input output devices 323, memory resource 319, andmicroprocessor 321. The bus also communications with course module 350,which has been previously explained throughout this specification.

In an example, the system also has a block chain process and relatedblock chain information 355 that is distributed through a network. In anexample, the system can take learning data, and perform a block chainprocess, including encryption and distribution for public or selectiveviewing.

In an example, the system has a wallet 360 provided in memory device ona client or other user device, including a server or other computingdevice. The wallet is secured and can be removable from the system bothelectrically and mechanically. In an example, the system also has aminer module coupled to a processing device via a bus or other device.Of course, there can be other variations, modifications, alternatives.

In an example, the system can be configured using a secure learningexperience using a encrypted encoded network. In an example, the methodincludes configuring information using a course module derived from aninteractive course platform coupled to a server and a network. In anexample, the course module comprises information provided from a humanuser coupled to a plurality of sensing devices, including at least animage capturing device, a motion sensor, and an ambient sensor. Forsecurity, the method assigns a time and date stamp on the course module,and then processes information using the course module to validate itagainst a predetermined quality metric, coding format, and ratificationinformation to configure a second course module in a canonical format.In an example, the method processes information using the course modulein the second format using an encryption process. The method thenconfigures information using the course module on a public ledge in ablock chain configuration.

FIG. 4 is a more detailed diagram of an alternative processing system400 according to an example of the present invention. As shown, thesystem has a bus that communicates to a sensor array 403, which includesboth passive sensors 407 and active sensors 409. The bus also has inputoutput device 405, processor 411, memory resource 413, which is coupledto mass storage 415 including documents, templates, and otherconfigurations. A course module 350 is coupled to the bus. In anexample, the system also has a block chain process and related blockchain information 355 that is distributed through a network. In anexample, the system can take learning data, and perform a block chainprocess, including encryption and distribution for public or selectiveviewing.

In an example, the system has a wallet 360 provided in memory device ona client or other user device, including a server or other computingdevice. The wallet is secured and can be removable from the system bothelectrically and mechanically. In an example, the system also has aminer module coupled to a processing device via a bus or other device.Of course, there can be other variations, modifications, alternatives.

FIG. 5 is a more detailed diagram of an alternative processing system500 according to an example of the present invention. As shown, thesystem has a bus that communicates to a sensor array 403, which includesboth passive sensors 407 and active sensors 409. The bus also has inputoutput device 405, processor 511, which can include artificialintelligence processors, digital signal processors, and otherprocessors, memory resource 413, which is coupled to mass storage 415including documents, templates, and other configurations. An artificialintelligence module 501 is also coupled to the memory resource. Theartificial intelligence module includes configurations that are learnedand used in accordance to this example with the course module 350. Acourse module is coupled to the bus. In an example, the system also hasa block chain process and related block chain information 355 that isdistributed through a network. In an example, the system can takelearning data, and perform a block chain process, including encryptionand distribution for public or selective viewing.

In an example, the system has a wallet 360 provided in memory device ona client or other user device, including a server or other computingdevice. The wallet is secured and can be removable from the system bothelectrically and mechanically. In an example, the system also has aminer module coupled to a processing device via a bus or other device.Of course, there can be other variations, modifications, alternatives.

FIG. 6 is a simplified flow diagram 600 of an integrated data processingprocess according to an example of the present invention. In an example,the method begins with start, step 601. In an example, the inventionprovides a method of using the meta module process of capturing data andprocessing the data for feedback to a user, e.g., learner, student, orother human entity. In an example, the present process provides for asingle integrated application to perform various processes that can beimplemented using a combination of hardware and software. In an example,the method includes providing information related to a course in a firstformat, such as a video, a text book, or any combination thereof. In anexample, the course can be sourced from an off-line class-room course.

In an example, the method includes transferring the information in thefirst format to a server device. The server device can be coupled to aplurality of computers on a world wide network of computers, such as theInternet. In an example, the method includes storing the information inthe first format into a memory resource coupled to the server device.

In an example, the method includes processing the information related tothe course on the server device to convert the course into a secondformat, the second format comprising a plurality of templates and avideo. The second format is suitable for using the course in an on-linemanner through the Internet and client devices.

The method configures the course in the second format with a metamodule. As noted, the meta module comprises an artificial intelligencemodule. In an example, the artificial intelligence module comprises aplurality of input interfaces each of the input interfaces coupled to anode. In an example, each of the nodes comprises a weighing function. Inan example, each of the nodes is coupled to an output interface.

In an example, the artificial intelligence module comprises a neuralnetwork process configured on a neural network process, wherein theplurality of nodes comprises at least 1 million nodes. In an example,the artificial intelligence module comprises a neural network processconfigured on a processing device.

In an example, the method initiates 603 capturing data from a pluralityof sensing devices, which can be internal or external, passive, oractive in an example, to teach the neural network process including theplurality of nodes with selected weighing functions.

In an example, the method includes configuring 605 the course coupled tothe artificial intelligence module to a plurality of external datacapturing devices, each of the devices being associated with a weighingfunction for the neural network process. Each of the plurality ofexternal data captures devices being spatially disposed on a mobilecomputing device or other device. In an example, the mobile computingdevice is coupled to the world wide network of computers.

In an example, the method includes initiating 607 use of the course inthe second format coupled to the artificial intelligence module from themobile computing device, among other client devices, and capturing datafrom a user of the mobile computing device from each of the plurality ofexternal data capturing devices, while the user is actively interactingwith the course in the second format from the mobile computing device.

In an example, while using the module associated with the course, if anyrules and/or decisions related to neural network process is triggered,step 609, the method processes information 613 associated with suchrules and/or decisions, and provides feedback 615 to the user in anexample. As further shown, n an example, the method includestransferring data from each of the plurality of external data capturingdevices from the mobile computing device to the artificial intelligencemodule and, thereafter, outputting a feedback 615 from the artificialintelligence module to the user of the course.

In an example, the method also includes finalizing use of the course inthe second format; and initiating a test associated with the course.Optionally, the method includes finalizing use of the course in thesecond format; initiating a test associated with the course; passing thetest; and transferring a credit for the course to the user of the mobiledevice and the course. The method ends at stop, step 621. Of course,there can be other variations, modifications, and alternatives.

Optionally, the method can also includes transferring spatial movementinformation from a wearable device to the mobile computing device. Thewearable device can be a watch, a suit, a vest, a headset, a pair ofglasses, a pair of pants, shoes, or other wearable device. The wearabledevice can include a plurality of sensing devices spatially disposed onthe device. A wireless or wired transceiver or transmitter can transmitinformation from each of the sensors to the meta processor.

In an example, the course module and other elements can be implementedusing a mobile device. In an example, the mobile device is a lap topcomputer, a tablet, an iPad, a Smart phone, or other mobile device. Ofcourse, there can be other variations, modifications, and alternatives.

FIG. 7 is a simplified flow diagram of a power management and miningprocess according to an example of the present invention. As shown, themethod 700 begins with start, step 701, initiate system, step 703,processing power, step 705, process data, step 707, decision, step 709,with no and yes branch, including process, step 713, feedback, step 715,and stop, step 721, and minor module, step 755. The process is a methodfor operating a computing apparatus. In an example, the apparatus has abus device configured to be an interface for transmission of data, amicro processing device coupled to the bus device, an image capturingdevice coupled to the bus device, a memory resource coupled to the busdevice, and a wallet device provided in the memory resource orinsertable as a separate device to be coupled to the bus device. In anexample, crypto currency can be stored in the wallet device. In anexample, the apparatus has a network interface coupled to the busdevice, a power supply coupled to each of the bus device, the microprocessing device, the image capturing device, the memory resource, andthe network interface.

In an example, a learning module coupled to the bus device. In anexample, the learning module comprises a course module configured tooutput a course directed to a subject. In an example, the coursecomprising a plurality of templates and a video associated with thesubject.

In an example the apparatus has a sensor input handler coupled to aplurality of sensing devices, a video input handler coupled to the busdevice, and an artificial intelligence (“AI”) module comprising aplurality of nodes numbered from 1 through N, where N is an integer of10,000,000 or less, and an AI output each of the nodes being coupled thesensor input handler, the video input handler, or another input handler.

In an example, an output handler is coupled to the processing device,and configured to the AI output.

As shown, the method initiates a system having the apparatus, step 703.The method processes power for the system, including the microprocessingdevices, 705. In an example, the method also processes data using avariety of artificial intelligence processes, step 707.

In an example, the method performs various decisions step 709. In anexample, the method processes the decision, such as a feedback process,step 713. In an example, the method includes a feedback process 715coupled to the output handler to intake information from the AI outputand configured to reduce a number of cycles executed by the microprocessing device from a first predetermined number range to a secondpredetermined number range to reduce a power consumption of the microprocessing device from a first predetermined power range to a secondpredetermined power range.

In an example, the method also performs a mining process using a minermodule, 755, in an example. The miner module is coupled to an output ofthe learning module to initial a process to determine if a coin deviceassociated with a user of the course module by a user, issuing the coindevice to the user, and storing the coin device in the wallet device,while the coin device is configured in an encrypted form. In an example,the method outputs the issued coin device.

In an example, the feedback process comprises a plurality ofcharacteristics to allow the user to achieve a predetermined scorewithin a range for the course. In an example, the learning modulecomprising a natural language processor is configured to process thetext information to identify a characteristic of the user in associationwith the course. In an example, the plurality of nodes comprises atleast 1 million nodes; and wherein the plurality of sensors comprises acamera, a keyboard, an accelerometer sensor, an rf sensor, a gyroscope,a chemical sensor, a temperature sensor, a microphone, or other inputdevice. In an example, the course is related to science, math, English,history, psychology, engineering, business, finance, accounting, or anyother college course subject.

In an example, the apparatus further comprises a wearable devicecomprising a set of sensors to characterize movement, orientation,temperature, heart rate, breathing rate, oxygen, and other parameters ofthe user while interacting with the course, the wearable device beingcoupled to an input of the artificial intelligence module. In an examplethe housing is shock proof. In an example, the artificial intelligencemodule comprises a neural network module configured to receiveinformation from the image capturing device and output a learningcharacteristic of the user. In an example, the artificial intelligencemodule comprises a neural network module configured to receiveinformation associated with a facial expression and an eye response fromthe image capturing device and output a learning characteristic of theuser. In an example, the apparatus has a time and location devicecoupled to the learning module and configured to transfer a spatiallocation information and a time information to the artificialintelligence module. In an example, the first predetermined power rangeis associated with a first mode associated with the course, and thesecond predetermined power range is associated with a second modeassociated with the course; and each of the first mode and the secondmode is one of a plurality of modes numbered from 1 to M, where M is aninteger greater than 10. In an example, the coin device is one of aplurality of coin devices, the coin device is configured to be exchangedwith a Bitcoin, an Ether, or other cryptocurrency. Of course, there canbe other variations, modifications, and alternatives.

In an example, various hardware elements of the invention can beimplemented using a smart phone with a capture image of a user accordingto an embodiment of the present invention. As shown, the smart phoneincludes a housing, display, and interface device, which may include abutton, microphone, or touch screen. Preferably, the phone has ahigh-resolution camera device, which can be used in various modes. Anexample of a smart phone can be an iPhone from Apple Computer ofCupertino California. Alternatively, the smart phone can be a Galaxyfrom Samsung or others.

In an example, the smart phone includes the following features (whichare found in an iPhone from Apple Computer, although there can bevariations), see www.apple.com, which is incorporated by reference. Inan example, the phone can include 802.11b/g/n Wi-Fi (802.11n 2.4 GHzonly), Bluetooth 2.1 +EDR wireless technology, Assisted GPS, Digitalcompass, Wi-Fi, Cellular, Retina display, 5-megapixel iSight camera,Video recording, HD (720 p) up to 30 frames per second with audio, Photoand video geotagging, Three-axis gyro, Accelerometer, Proximity sensor,and Ambient light sensor. Of course, there can be other variations,modifications, and alternatives.

An exemplary electronic device may be a portable electronic device, suchas a media player, a cellular phone, a personal data organizer, or thelike. Indeed, in such embodiments, a portable electronic device mayinclude a combination of the functionalities of such devices. Inaddition, the electronic device may allow a user to connect to andcommunicate through the Internet or through other networks, such aslocal or wide area networks. For example, the portable electronic devicemay allow a user to access the internet and to communicate using e-mail,text messaging, instant messaging, or using other forms of electroniccommunication. By way of example, the electronic device may be a modelof an iPod having a display screen or an iPhone available from AppleInc.

In certain embodiments, the device may be powered by one or morerechargeable and/or replaceable batteries. Such embodiments may behighly portable, allowing a user to carry the electronic device whiletraveling, working, exercising, and so forth. In this manner, anddepending on the functionalities provided by the electronic device, auser may listen to music, play games or video, record video or takepictures, place and receive telephone calls, communicate with others,control other devices (e.g., via remote control and/or Bluetoothfunctionality), and so forth while moving freely with the device. Inaddition, device may be sized such that it fits relatively easily into apocket or a hand of the user. While certain embodiments of the presentinvention are described with respect to a portable electronic device, itshould be noted that the presently disclosed techniques may beapplicable to a wide array of other, less portable, electronic devicesand systems that are configured to render graphical data, such as adesktop computer.

In the presently illustrated embodiment, the exemplary device includesan enclosure or housing, a display, user input structures, andinput/output connectors. The enclosure may be formed from plastic,metal, composite materials, or other suitable materials, or anycombination thereof. The enclosure may protect the interior componentsof the electronic device from physical damage, and may also shield theinterior components from electromagnetic interference (EMI).

The display may be a liquid crystal display (LCD), a light emittingdiode (LED) based display, an organic light emitting diode (OLED) baseddisplay, or some other suitable display. In accordance with certainembodiments of the present invention, the display may display a userinterface and various other images, such as logos, avatars, photos,album art, and the like. Additionally, in one embodiment, the displaymay include a touch screen through which a user may interact with theuser interface. The display may also include various function and/orsystem indicators to provide feedback to a user, such as power status,call status, memory status, or the like. These indicators may beincorporated into the user interface displayed on the display.

In an embodiment, one or more of the user input structures areconfigured to control the device, such as by controlling a mode ofoperation, an output level, an output type, etc. For instance, the userinput structures may include a button to turn the device on or off.Further the user input structures may allow a user to interact with theuser interface on the display. Embodiments of the portable electronicdevice may include any number of user input structures, includingbuttons, switches, a control pad, a scroll wheel, or any other suitableinput structures. The user input structures may work with the userinterface displayed on the device to control functions of the deviceand/or any interfaces or devices connected to or used by the device. Forexample, the user input structures may allow a user to navigate adisplayed user interface or to return such a displayed user interface toa default or home screen.

The exemplary device may also include various input and output ports toallow connection of additional devices. For example, a port may be aheadphone jack that provides for the connection of headphones.Additionally, a port may have both input/output capabilities to providefor connection of a headset (e.g., a headphone and microphonecombination). Embodiments of the present invention may include anynumber of input and/or output ports, such as headphone and headsetjacks, universal serial bus (USB) ports, IEEE-1394 ports, and AC and/orDC power connectors. Further, the device may use the input and outputports to connect to and send or receive data with any other device, suchas other portable electronic devices, personal computers, printers, orthe like. For example, in one embodiment, the device may connect to apersonal computer via an IEEE-1394 connection to send and receive datafiles, such as media files. Further details of the device can be foundin U.S. Pat. No. 8,294,730, assigned to Apple, Inc.

In embodiments of the present invention, various uses of blockchaincoding may provide authentication and verification of the educationprocess. For example, course materials provided directly from aninstitution (e.g. lecturer, professor) or the modified course materialsprovided to the user using embodiments of the present invention may beencoded into a first block (not necessarily the ordinal first block) ina block chain. In various embodiments, the authentication may alsoinclude proof of accreditation of the course and/or the institutionitself. In some embodiments, prior to hashing, the materials may beencoded with a provider private key. In some embodiments, prior tohashing, the materials may be authenticated by an accrediting body witha private key. It is contemplated that the first block can help verifythe materials provide to the user are genuine and authorized.

In some embodiments, education materials provided to the user arecustomized for each user, for example, by adding the user's name,address, student identifier, or the like to the documents, videos, etc.Accordingly, the blockchain hash of educational materials for differentusers should also be different. Such techniques are usable inembodiments where course materials are highly valuable, and onlystudents who enroll in the course should be able to receive credit orcertification for the course. In such cases, if a third-party attemptsuse the course materials without registering, the third party cannotreceive accreditation for the course. This is because the blockcorresponding to the course materials in the third party's blockchainwill not be authenticated with the third party's name.

Next, in various embodiments, if a user completes a course successfully,an additional block is added to the blockchain. The blockchain hash maybe performed upon the student data (e.g. name, student ID, course, time,etc.), one or more certificates of completion, course information (e.g.institution name, professor name, course name, credit hours, etc.),grade, and the like. In various embodiments, the user may beauthenticated by other means, such as social security number, privatekey, password, or the like. In various embodiments, the new blockincludes a hash of the previous block (e.g. proof that the educationmaterials used were authorized) plus the hash of the above userinformation. As a result, the blockchain can be used to prove that theauthorized user completed a specific course authorized by a specificaccredited institution.

In various embodiments of the present invention, as a user repeats theabove process for different courses, additional blocks are added to theblockchain. The blockchain thus can verify the user's credits, grades,etc.; can verify that the course was provided by an authorizedinstitution, and can verify that the user was authorized to take thecourse. In various embodiments, various educational providers,educational institutions, and other distributed stakeholders by storeblockchains geared towards educational achievements.

In some embodiments, transactions within the process described above mayalso include payment information. In various embodiments, paymentinformation may be made using available coin offerings. In otherembodiments, payment information may be made using a custom coinoffering directed to education on all different levels, education frominstitutions of higher learning, offerings in conjunction witheducational 529 plans, and the like. The inventor is not currently awareof such educationally-directed coin offerings, but believes they may beused in various embodiments of the present invention.

In an example, the invention also includes a method for a securelearning experience using an encrypted encoded network. The methodincludes configuring a course module information derived from aninteractive course platform coupled to a server and a network. In anexample, the course module comprises information provided from a humanuser coupled to a plurality of sensing devices, including at least animage capturing device, a motion sensor, and an ambient sensor. Themethod includes assigning a time and date stamp on the course module andprocessing the course module to validate it against a predeterminedquality metric, coding format, and ratification information to configurea second course module in a canonical format. The method includesprocessing the course module in the second format using an encryptionprocess and configuring the course module on a public ledger in a blockchain configuration.

In various embodiments of the present invention, using one or more ofthe above-described sensors, associated with the user's client system(e.g. smart phone, laptop, etc.) the user's actions may be monitored.More specifically, while the user is interacting with course materials,e.g. video or audio lectures, interactive presentations, or the like,actions of the user may be captured. Types of user actions may include,the user's use of a keyboard or mouse; the user moving or tilting theuser's client system; the movement of the user's head, eyes, hands;whether the user's eyes are open; whether the user is talking orlistening to a different audio source; and the like. In variousembodiments, the user's interactions may be stored on the client systemand/or uploaded to a remote (educational) server.

In various embodiments various methods may be used to determine whetherthe user actions indicate the user is paying attention to the lecture orpresentation or not. As an example, in one embodiment, the video cameraon the (user's) client system may monitor whether the user's head isfacing the client system display, whether the user's eyes are directedtoward the screen, and the like. Next, based upon an analysis of thevideo image, it can be determined whether the user is paying attentionor not. In one example, the amount of time the user's eyes face thescreen per time period (e.g. per minute) is computed, and compared toother time periods. In one embodiment if there is a negative slope overmultiple time periods (e.g. two, three or more), the remote server mayconclude that the user is not paying attention. In one example, theamount of time the user's eyes faces the display per minute is asfollows: 1 minute, 45 seconds; 2 min, 50 sec; 3 min, 40 sec; 4 min, 40sec; 5 min, 30 sec; 6 min 20 sec; etc. Based upon this example, becausethere is a pronounced negative slope over several time periods, theremote server may determine that by the sixth minute, the user is notpaying attention to the lecture. In other embodiments, other types ofmetrics, and other periods of time may be utilized. For example, theuser may be asked to answer questions within a certain amount of time;to strike a key on a keyboard; to say a word; or the like. In variousembodiments of the present invention, the metrics used to make the abovedeterminations may be termed a user efficiency factor or a learningutilization factor.

In various embodiments, based upon the user efficiency factor, theclient system and/or the remote system may provide immediate feedback tothe user, or feedback at a later time. In some embodiments, the feedbackmay be positive feedback to the user, praising the user for theirefforts and/or further encouraging the user. Examples of positivefeedback may include giving the user rewards for paying attention to alecture, or the like, such as points the user may spend onin-application purchases (e.g. stickers, outfits, decorations,additional courses or lectures, etc.); points the user may apply toreal-world items (e.g. coffee cards, Amazon gift cards, etc.); cryptocurrency (e.g. Bitcoin, Ethereum, etc.); and the like.

In some embodiments of the present invention, the user rewards mayinclude assigning the user one or more block chain (e.g. cryptocurrency) processes. In such a case, the remote server may include oneor more dedicated hardware crypto currency mining rigs, and/or blockchain mining software. When a reward is earned by a user, the remoteserver may instantiate a copy of the block chain mining software andassign the process to the user. It is contemplated that multiple usersof the remote server may have one or more mining processes associatedtherewith. In some cases, each user may mine independently; in othercases all uses of the educational server(s) may form a mining pool; andin still other cases, groups of users may form smaller mining pools,e.g. groups of students taking a specific course; or the like. In someembodiments, the block-chain mining may be associated with a generalpurpose crypto-currency (e.g. Bitcoin), and in other embodiments, themining may be associated with an educational crypto-currency, and thelike.

In some embodiments of the present invention, the user efficiency factormay lead to an inference that the user is not actively paying attentionand/or that the user is not actively learning. Accordingly, differentways of attempting to get the user's attention may be performed. In somecases, the actions may be performed by the user's client system with orwithout receiving further instructions from the remote server. Examplesof actions that may be performed include: modifying (e.g. increasing)the volume of audio; modifying the presentation (e.g. increasingbrightness or contrast, providing bright flashes); playing audio output(e.g. spoken words, music, a beep sound, sound effects); modifying oroverlaying graphics associated with the presentation (e.g. changing abackground to orange, overlaying an icon of a flying bird over thepresentation, and the like); pausing the lecture or presentation;presenting one or more questions about the lecture; and the like.

In some embodiments, other types of actions may be performed when theuser learning utilization indicates inattention, such as reducingcomputer and network resource allocation and usage. Examples of reducingresource usage includes reducing the frame rate or resolution of a videopresentation, reducing audio and/or video bit rate, decreasing a qualityof service level, increasing a client response latency (e.g. decreasinga user priority level), reducing a communication data rate or bandwidth,changing a communication channel (e.g. from 4G to 3G or Edge), and thelike. In light of the present patent disclosure, one of ordinary skillin the art will consider other alternatives that are still withinembodiments of the present invention.

In various embodiments of the present invention, it is contemplated thatover time, user actions from multiple users will be captured for eachlecture or presentation. Such data may be used to dynamically allocatecomputer and network resources intra-lecture (e.g. during the lecture).For example, if multiple users have a user action drop off during aspecific portion of a lecture or presentation, embodiments may reducethe computer and/or network resources used during the specific portionfor other users. Embodiments may also be used inter-lecture (e.g. fordifferent lectures) such that different levels of resources are utilizedor provided according to previous users' actions. As an example, a firstlecture may be output using 1× computer and network resources, whereas asecond lecture may be output using 0.8× computer and network resources.As can be seen, the user action may be used to adjust, e.g. reduceserver requirements (e.g. amount of memory, number of processor cores,number of virtual servers, and the like.) In various embodiments, ifphysical servers are owned and used, the number of servers, e.g.footprint may be reduced; and if the servers are subscribed from thecloud (e.g. AWS), the level of cloud service may be reduced. In variousembodiments, the computer and network resources described in theparagraph above may be adjusted as described in this paragraph.

In various embodiments, the user efficiency factor for a lecture orpresentation may be correlated with user performance for the lecture. Itis contemplated that numerous questions will be provided to the userduring a lecture, and the user answers them to complete the lecture. Insome embodiments, the user performance may include an efficiency factorthat indicates how many times the user submits answers to questions,before they answer all the question correctly; in some embodiments, theuser performance may include a persistence factor that indicates thenumber of times the user watches a lecture, until they answer all of thequestions correctly; and the like. In some embodiments, based upon theefficiency factor, persistence factor, and the like the user for alecture, user feedback may also be provided. For example, users may begiven a reward for having a high efficiency factor, users may be given areward for having a high persistence factor, and the like. Suchembodiments thus reward the user, not based upon one metric, butmultiple (potentially orthogonal) metric. In various embodiments, therewards discussed herein, e.g. points, coins, mining processes, and thelike, may be provided as feedback.

In some embodiments, the user performance may also be used by the remoteserver to provide feedback to the user. As an example, a user watchesthe first two lectures for a class at 9 pm and has a user performance of90%; the user watches the next lecture for the class at midnight and hasa user performance of 80%, and the user watches the next three lecturesfor the class at 3 am and has a user performance of 70%. In such a case,the user may be encouraged by embodiments to watch lectures earlier atnight, e.g. 9 pm, rather than late at night e.g. 3 am, as the user'sperformance has shown to suffer the later they watch the lecture. Suchfeedback may be given intra-lecture (as described above); may be giveninter-lecture (e.g. for different lectures for a particular class); andmay also be given inter-course (e.g. user feedback for one course givento the user when beginning a different course).

In some cases, the user feedback described above may be provided on oneor more dashboards provided to the user. As an example, the user may seea graph of their eyes facing the screen during a lecture; the user maysee a graph of their user efficiency points and their persistence pointsfor different lectures (e.g. net learner improvement); the user may seetheir net user efficiency for different classes (e.g. net learnerimprovement); and the like. In other embodiments, other types of dataassociated with the user for different lectures and or different classesmay also be provided to the user.

Having described various embodiments, examples, and implementations, itshould be apparent to those skilled in the relevant art that theforegoing is illustrative only and not limiting, having been presentedby way of example only. Many other schemes for distributing functionsamong the various functional elements of the illustrated embodiment orexample are possible. The functions of any element may be carried out invarious ways in alternative embodiments or examples.

Also, the functions of several elements may, in alternative embodimentsor examples, be carried out by fewer, or a single, element. Similarly,in some embodiments, any functional element may perform fewer, ordifferent, operations than those described with respect to theillustrated embodiment or example. Also, functional elements shown asdistinct for purposes of illustration may be incorporated within otherfunctional elements in a particular implementation. Also, the sequencingof functions or portions of functions generally may be altered. Certainfunctional elements, files, data structures, and so one may be describedin the illustrated embodiments as located in system memory of aparticular or hub. In other embodiments, however, they may be locatedon, or distributed across, systems or other platforms that areco-located and/or remote from each other. For example, any one or moreof data files or data structures described as co-located on and “local”to a server or other computer may be located in a computer system orsystems remote from the server. In addition, it will be understood bythose skilled in the relevant art that control and data flows betweenand among functional elements and various data structures may vary inmany ways from the control and data flows described above or indocuments incorporated by reference herein. More particularly,intermediary functional elements may direct control or data flows, andthe functions of various elements may be combined, divided, or otherwiserearranged to allow parallel processing or for other reasons. Also,intermediate data structures of files may be used and various describeddata structures of files may be combined or otherwise arranged.

In other examples, combinations or sub-combinations of the abovedisclosed invention can be advantageously made. The block diagrams ofthe architecture and flow charts are grouped for ease of understanding.However, it should be understood that combinations of blocks, additionsof new blocks, re-arrangement of blocks, and the like are contemplatedin alternative embodiments of the present invention.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that various modifications and changes may be made thereuntowithout departing from the broader spirit and scope of the invention asset forth in the claims.

The invention claimed is:
 1. A computing apparatus, the apparatuscomprising: a bus device configured to be an interface for transmissionof data; a processor; a plurality of sensing devices coupled to the busdevice, the plurality of sensing devices including an image capturingdevice; and a memory resource coupled to the bus device and including acourse module application; and a network interface coupled to the busdevice, wherein the course module application when executed by theprocessor causes the apparatus to: output a course directed to asubject, the course comprising a plurality of templates and a videoassociated with the subject; provide an artificial intelligence (AI)module comprising a plurality of nodes by performing a neural networkprocess, each of the nodes being coupled to one or more of the pluralityof sensing devices; perform a first feedback process to infer, by the AImodule using information from the one or more of the plurality ofsensing devices, whether a user is actively paying attention to thecourse, and to provide feedback to the user based on the inference, thefeedback including a recommended time for watching a lecture; determine,based on the inference of whether the user is actively paying attentionto the course, whether the user has earned a reward; in response todetermining that the user has earned the reward, and cause digitalcurrency to be generated and provide the generated digital currency tothe user as the reward; determine, based on the inference of whether theuser is actively paying attention to the course, whether the user is notactively paying attention to the course; in response to determining thatthe user is not actively paying attention to the course, performing anaction configured to get the attention of the user; and perform naturallanguage processing to process text information to identify acharacteristic of the user in association with the course.
 2. Theapparatus of claim 1 wherein the first feedback process comprises aplurality of characteristics to allow the user to achieve apredetermined score within a range for the course.
 3. The apparatus ofclaim 1 wherein the digital currency is crypto currency.
 4. Theapparatus of claim 3 wherein the plurality of nodes comprises at least 1million nodes; and wherein the plurality of sensing devices comprises acamera, a keyboard, an accelerometer sensor, an rf sensor, a gyroscope,a chemical sensor, a temperature sensor, a microphone, or combinationsthereof.
 5. The apparatus of claim 1 further comprising a wearabledevice comprising a set of sensors to characterize movement,orientation, temperature, heart rate, breathing rate, oxygen, and otherparameters of the user while interacting with the course, the wearabledevice being coupled to an input of the artificial intelligence module.6. The apparatus of claim 1 wherein the artificial intelligence modulecomprises a neural network module configured to receive information fromthe image capturing device and output a learning characteristic of theuser.
 7. The apparatus of claim 1 wherein the artificial intelligencemodule comprises a neural network module configured to receiveinformation associated with a facial expression and an eye response fromthe image capturing device and output a learning characteristic of theuser.
 8. The apparatus of claim 1, wherein the course module applicationwhen executed by the processor further causes the apparatus to: performa second feedback process configured to intake information output by theAI module and to reduce a number of cycles executed by the microprocessing device from a first predetermined number range to a secondpredetermined number range to reduce a power consumption of the microprocessing device from a first predetermined power range to a secondpredetermined power range.
 9. The apparatus of claim 1, wherein thefeedback to the user includes positive feedback to the user in responseto the apparatus inferring that the user is actively paying attention tothe course.
 10. The apparatus of claim 1, wherein the digital currencyis an educationally-directed crypto currency.
 11. The apparatus of claim1, wherein the digital currency is generated using dedicated cryptocurrency mining hardware included in a remote server.
 12. The apparatusof claim 1, wherein the digital currency is generated in the apparatus.13. A method of using a computing apparatus comprising: in the computingapparatus comprising a bus device configured to be an interface fortransmission of data; a processor; a plurality of sensing devicescoupled to the bus device, the plurality of sensing devices including animage capturing device; a memory resource coupled to the bus device andincluding a course module application; a time and location deviceconfigured to transfer spatial location information and time informationto an artificial intelligence module; and a network interface coupled tothe bus device, executing the course module application by the processorcauses the apparatus to: output a course directed to a subject, thecourse comprising a plurality of templates and a video associated withthe subject; provide the artificial intelligence (AI) module comprisinga plurality of nodes by performing a neural network process, each of thenodes being coupled to one or more of the plurality of sensing devices;perform a first feedback process to infer, by the AI module usinginformation from the one or more of the plurality of sensing devices,whether a user is actively paying attention to the course, and toprovide feedback to the user based on the inference, the feedbackincluding a recommended time for watching a lecture; determine, based onthe inference of whether the user is actively paying attention to thecourse, whether the user has earned a reward; in response to determiningthat the user has earned the reward, cause crypto currency to begenerated and provide the generated crypto currency to the user as thereward; determine, based on the inference of whether the user isactively paying attention to the course, whether the user is notactively paying attention to the course; and in response to determiningthat the user is not actively paying attention to the course, performingan action configured to get the attention of the user.
 14. The method ofclaim 13 wherein the first predetermined power range is associated witha first mode associated with the course, and the second predeterminedpower range is associated with a second mode associated with the course;and each of the first mode and the second mode is one of a plurality ofmodes numbered from 1 to M, where M is an integer greater than
 10. 15. Amethod of using a computing apparatus, the method comprising: in theapparatus including a bus device configured to be an interface fortransmission of data; a processor; a plurality of sensing devicescoupled to the bus device, the plurality of sensing devices including animage capturing device; a memory resource coupled to the bus device andincluding a course module application; and a network interface coupledto the bus device, executing the course module application by theprocessor to: output a course directed to a subject, the coursecomprising a plurality of templates and a video associated with thesubject; provide an artificial intelligence (AI) module comprising aplurality of nodes by performing a neural network process, each of thenodes being coupled to one or more of the plurality of sensing devices;perform a first feedback process to infer, by the AI module usinginformation from the one or more of the plurality of sensing devices,whether a user is actively paying attention to the course, and toprovide feedback to the user based on the inference, the feedbackincluding a recommended time for watching a lecture; determine, based onthe inference of whether the user is actively paying attention to thecourse, whether the user has earned a reward; in response to determiningthat the user has earned the reward, cause crypto currency to begenerated and provide the generated crypto currency to the user as thereward; determine, based on the inference of whether the user isactively paying attention to the course, whether the user is notactively paying attention to the course; and in response to determiningthat the user is not actively paying attention to the course, performingan action configured to get the attention of the user, wherein thenetwork interface is coupled to a network, and wherein the course moduleapplication when executed by the processor further causes the apparatusto: in response to inferring that the user is not actively payingattention to the course, cause an allocation, a usage, or both of anetwork resource of the network to be reduced.